Content Based Image Retrieval Using Deep Learning Process

Abstract: CBIR uses image content features to search and retrieve digital images from a large database. A variety of visual feature extraction techniques have been employed to implement the searching purpose. Due to the computation time requirement, some good algorithms are not been used.
The retrieval performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurements. The ultimate aim of the proposed method is to provide an efficient algorithm to deal with the above mentioned problem definition.
Here the Deep Belief Network (DBN) method of deep learning is used to extract the features and classification and is an emerging research area, because of the generation of large volume of data. The proposed method is tested through simulation in comparison and the results show a huge positive deviation towards its performance.Keywords: Image Retrieval, Deep Learning, data analysis, image extraction
INTRODUCTION
In this era, technology upgrades to its maximum with the help of creativity and innovation, with such an ideas in the field of ANN, the basic module is said to be image processing stream so that most of the systems will map the inputs to its outputs with varied uncertainty logic [1].
The image will be considered as the digital formation and it will be decimated to its corresponding bits. The classification of image or video in the existing systems seems difficult due to its methodology works with the file name search and not the content inside it [2]. Depending upon the query given by the user the ANN should have to classify the content with various attributes.
Our proposed algorithm deal with Deep Learning methods, in which it confines each and every data, learns the contents by separating its features to the deep bottom. The database itself maintains a separate individual data centre that will contain a finite most significant amount of features [3]. Deep learning method shows its maximum performance to its extent and plays a smart extraction of the content from the data, which is on process [4].
Deep learning is one of the classifications of soft computing phenomenon in which extraction of data from millions of segregated images can be retrieved using this phenomenon [5]. The retrieval performance of a content-based image retrieval system crucially depends on the feature representation and similarity measurement, which have been extensively studied by multimedia researchers for decades.
Although a variety of techniques have been proposed, it remains one of the most challenging problems in current content-based image retrieval (CBIR) research, which is mainly due to the well-known “semantic gap” issue that exists between low-level image pixels captured by machines and high-level semantic concept perceived by humans. From a high-level perspective, such challenge can be rooted to the fundamental challenge of Artificial Intelligence (AI) that is, how to build and train intelligent machines like human to tackle real-world tasks [6]-[8].
Figure 1: CBIR system with Deep Learning
Machine learning is one promising technique that attempts to address this challenge in the long term [9]. Recent years have witnessed some important advanced new techniques in machine learning. Deep learning is the part of machine learning, which includes a family of machine learning algorithms that attempt to model high-level abstractions in data by employing deep architectures composed of multiple non-linear transformations [10].
Unlike traditional machine learning techniques that are often using “shallow” architectures, deep learning mimics the human brain that is organized in a deep architecture and processes information through multiple stages of transformation and representation [11]. By exploring deep architecture features at multiple levels of abstracts from data automatically, deep learning methods allow a system to learn complex functions that directly map raw sensory input datum to the output, without relying on human-crafted features using domain knowledge [12].
Many recent studies have reported encouraging results for applying deep learning techniques to a variety of applications, including speech recognition, object recognition, and natural language processing, among others. Inspired by the successes of deep learning, in this paper, we attempt to explore deep learning methods with application to CBIR tasks.
Despite much research attention of applying deep learning for image classification and recognition in computer vision, there is a still limited amount of attention focusing on the CBIR applications. In the proposed method, we investigate deep learning methods for learning feature representations from the images and their similarity measures towards CBIR tasks [13]-[15].
RELEVANCE OF WORK
The incomplete annotation issue in text based image retrieval will degrade the retrieval performance of the searching process [6]. Query by Image Retrieval (QBIR) has evolved into a necessary [6] module in which the contents of the images are extracted to search the images from the database. But CBIR system also faces many challenging problems because of the large volume of the database constrain, the difficulty in both people and computer understanding the images, the difficulty of creating a query and the issue of evaluating results properly.
In our method, we explore an alternative strategy for searching an image database [7] in which the content is expressed in terms of an image and its multiple features are extracted using different image feature extraction algorithms. These features are analyzed with the features of image database and the most similar images are retrieved using an efficient index based sorting algorithm [16].
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